def main():
args = get_arguments()
print(args)
np.random.seed(args.seed)
dataset, train_img_feature, train_data = get_train_data(args)
dataset, test_img_feature, test_data, val_answers = get_test_data(args)
train_X = [train_data[u'question'], train_img_feature]
train_Y = np_utils.to_categorical(train_data[u'answers'], args.nb_classes)
test_X = [test_data[u'question'], test_img_feature]
test_Y = np_utils.to_categorical(val_answers, args.nb_classes)
model_name = importlib.import_module("models."+args.model)
model = model_name.model(args)
model.compile(loss='categorical_crossentropy', optimizer=args.optimizer, metrics=['accuracy'])
model.summary() # prints model layers with weights
history = model.fit(train_X, train_Y, batch_size = args.batch_size, nb_epoch=args.nb_epoch, validation_data=(test_X, test_Y))
return history.history
def main():
print(colored('Drone Faces Main', 'blue'))
print(colored('Gather Arguments...', 'blue'))
args = get_arguments()
print_args( args )
### Load a trained model and fly the drone ###
if args.fly_tello:
pass
# fly( args )
elif args.train_model:
pass
elif args.load_images:
load_images( args )
print(colored('Whole Program has finished gracefully.', 'blue'))
def main():
# Argument parsing
args = argparse.ArgumentParser()
args = get_arguments()
args.device = torch.device('cuda', args.gpu_id)
# dataset/transform setting
if args.in_dataset in ['cifar10']:
args.num_classes = 10
elif args.in_dataset in ['cifar100']:
args.num_classes = 100
# Get Dataloader
transform = get_transform(args)
train_dataloader, test_dataloader = globals()[args.in_dataset](args,
transform)
# Get architecture
net = get_architecture(args)
# Get optimizer, scheduler
optimizer, scheduler = get_optim_scheduler(args, net)
CE_loss = nn.CrossEntropyLoss()
path = './checkpoint/' + args.in_dataset + '/' + args.arch + '_trial_' + args.trial
best_acc = 0
for epoch in range(args.epoch):
train(args, net, train_dataloader, optimizer, scheduler, CE_loss,
epoch)
acc = test(args, net, test_dataloader, optimizer, scheduler, CE_loss,
epoch)
scheduler.step()
if best_acc < acc:
best_acc = acc
if not os.path.isdir('checkpoint/' + args.in_dataset):
os.makedirs('checkpoint/' + args.in_dataset)
torch.save(net.state_dict(), path)
# log model
mlflow.pytorch.log_model(model, "models")
def main(config):
# Ser directories (or create if they don't exist)
set_dirs(config)
# Get data loader for imaging dataset.
img_loader = Loader(config, dataset_name="NucleiDataset")
# Get data loader for RNA dataset.
rna_loader = Loader(config, dataset_name="RNADataset")
# Start training and save model weights at the end
train(config, [img_loader, rna_loader], save_weights=True)
if __name__ == "__main__":
# Get parser / command line arguments
args = get_arguments()
# Get configuration file
config = get_config(args)
# Summarize config and arguments on the screen as a sanity check
print_config_summary(config, args)
# --If True, start of MLFlow for experiment tracking:
if config["mlflow"]:
# Experiment name
mlflow.set_experiment(experiment_name=config["model_mode"]+"_"+str(args.experiment))
# Start a new mlflow run
with mlflow.start_run():
# Run the main
main(config)
else:
# Run the main
main(config)
def main():
args = get_arguments()
env = get_environment()
# Called from cli, directory is required in this mode
if args.directory:
# Ensure path exists before continuing
if not os.path.isdir(args.directory):
log.critical('Directory not found. Exiting. "{}"'.format(
args.directory))
sys.exit(1)
# Script called from cli with recursive flag set, ignore all other flags if this is set
if args.recursive:
dirs_scanned = 0
trailers_downloaded = 0
trailers_not_found = 0
trailers_previously_dl = 0
log.info('Recursive mode enabled. Scanning "{}".'.format(
args.directory))
# Loop through each subdirectory
for sub_dir in os.listdir(args.directory):
path = os.path.join(args.directory, sub_dir)
if os.path.isdir(path):
directory = Movie_Folder(path)
if directory.has_movie:
dirs_scanned += 1
if not directory.has_trailer:
log.info('No Local trailer found in "{}"'.format(
directory.directory))
if _download_trailer(directory):
trailers_downloaded += 1
else:
trailers_not_found += 1
else:
log.info('Trailer already exists. "{}"'.format(
directory.trailer_filename))
trailers_previously_dl += 1
else:
log.warning(
'Assuming this is not a movie folder. No movie file found in "{}"'
.format(directory.directory))
log.info(
'------------------------------------------------------'
)
log.info('Done!! Enjoy your new trailers!')
log.info('Total directories scanned = {}'.format(dirs_scanned))
log.info(
'Total trailers downloaded = {}'.format(trailers_downloaded))
log.info(
'Total trailers not found = {}'.format(trailers_not_found))
log.info('Trailers previously found = {}'.format(
trailers_previously_dl))
# Script called from cli without recursive flag set with directory only
elif not args.year and not args.title:
log.info('Single Directory mode enabled. Scanning "{}"'.format(
args.directory))
directory = Movie_Folder(args.directory)
if directory.has_movie:
if not directory.has_trailer:
log.info('No Local trailer found in "{}"'.format(
directory.directory))
_download_trailer(directory)
log.info(
'------------------------------------------------------'
)
else:
log.info('Trailer already exists. "{}"'.format(
directory.trailer_filename))
log.info(
'------------------------------------------------------'
)
else:
log.info(
'Assuming this is not a movie folder. No movie file found in "{}"'
.format(directory.directory))
log.info(
'------------------------------------------------------')
# Called from cli with year and title set
elif args.title and args.year:
# Start processing single directory from radarr env variables
log.info('Single directory mode initiated for "{} ({})"'.format(
args.title, args.year))
directory = Movie_Folder(args.directory)
directory.set_title_year(args.title, args.year)
if directory.has_movie:
if not directory.has_trailer:
log.info('No Local trailer found in {}'.format(
directory.directory))
_download_trailer(directory)
log.info(
'------------------------------------------------------'
)
else:
log.info('Trailer already exists. "{}"'.format(
directory.trailer_filename))
else:
log.info(
'Assuming this is not a movie folder. No movie file found in "{}"'
.format(directory.directory))
# Called from cli with imdbid set
elif args.imdbid:
log.info('Single directory mode initiated for imdb "{}"'.format(
args.imdbid))
directory = Movie_Folder(args.directory)
directory.imdb_id = args.imdbid
if directory.has_movie:
if not directory.has_trailer:
log.info('No Local trailer found in "{}"'.format(
directory.directory))
_download_trailer(directory)
log.info(
'------------------------------------------------------'
)
else:
log.info('Trailer already exists. "{}"'.format(
directory.trailer_filename))
log.info(
'------------------------------------------------------'
)
else:
log.info(
'Assuming this is not a movie folder. No movie file found in "{}"'
.format(directory.directory))
log.info(
'------------------------------------------------------')
# Called from cli with tmdbid set
elif args.tmdbid:
log.info('Single directory mode initiated for tmdb "{}"'.format(
args.tmdbid))
directory = Movie_Folder(args.directory)
directory.tmdb_id = args.tmdbid
if directory.has_movie:
if not directory.has_trailer:
log.info('No Local trailer found for "{}" in {}'.format(
directory.title, directory.year))
_download_trailer(directory)
log.info(
'------------------------------------------------------'
)
else:
log.info('Trailer already exists. "{}"'.format(
directory.trailer_filename))
else:
log.info(
'Assuming this is not a movie folder. No movie file found in "{}"'
.format(directory.directory))
# Not enough data was provided in arguments to process
else:
log.warning('Not enough info was provided. Exiting.')
sys.exit(1)
# Called from Radarr with environment variables set
elif env['valid']:
log.info('Single directory mode initiated from Radarr')
# Ensure directory exists before continuing
if not os.path.isdir(env['movie_dir']):
log.warning('Directory not found. Exiting. "{}"'.format(
env['movie_dir']))
sys.exit(1)
directory = Movie_Folder(env['movie_dir'],
title=env['title'],
year=env['year'],
tmdb=env['tmdbid'],
imdb=env['imdbid'])
if directory.has_movie:
if not directory.has_trailer:
log.info('No Local trailer found in "{}"'.format(
directory.directory))
_download_trailer(directory)
log.info(
'------------------------------------------------------')
else:
log.info('Trailer already exists. "{}"'.format(
directory.trailer_filename))
log.info(
'------------------------------------------------------')
else:
log.info('No movie file found in "{}"'.format(directory.directory))
log.info('------------------------------------------------------')
# Called without enough information to find appropriate trailer
else:
log.warning('Script called without enough valid information. Exiting')
#!/usr/bin/env python3
import os
from utils.logger import Logger
from utils.config import Config
from utils.environment import Env
from utils.arguments import get_arguments
__appName__ = 'TrailerTech'
__author__ = 'JsAddiction'
__version__ = '0.1.0'
__description__ = 'Download Trailers for your movie library.'
CONFIG_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)),
'settings.ini')
LOG_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)),
'TrailerTech.log')
env = Env()
args = get_arguments(__appName__, __description__, __version__)
config = Config(CONFIG_PATH)
logger = Logger(LOG_PATH,
config.log_level,
config.log_to_file,
quiet=args.quiet)